1.5-beta.2

Additions

• Added installation script install.sh
• Added ggplot2 to renjin package dependencies.

Package dynaml.probability

• Added ContMixtureRVBars a private class which can be instantiated using the apply method of ContinuousDistrMixture

/*
The following import is needed for an implicit parameter
which serves as the inner product space on the domain 
i.e. Inner products on Double
*/
import spire.implicits._

//Initialise the mixture components
val components = Seq(UnivariateGaussian(0.0, 1.0), UnivariateGaussian(-1.5, 0.2))

val gaussian_mixture = ContinuousDistrMixture[Double, Double, UnivariateGaussian](
  components, DenseVector(0.65, 0.35))

Package dynaml.models

• Added API starting points for implementations of stochastic mixture models. The key classes/traits are
  • StochasticProcessMixtureModel: The base class
  • ContinuousMixtureModel: An abstraction for mixture models building on subtypes of ContinuousProcessModel as the base processes. Offers implementation of only the predictiveDistribution() method.
  • GenContinuousMixtureModel: This builds mixture models based on stochastic processes which return predictive distributions in closed form, having mean, variance and error bars. Implements all methods except toStream(y) and getVectorSpace(num_dim) which are left to the user.

Package dynaml.models.gp & dynaml.models.stp

• Added classes GaussianProcessMixture, StudentTProcessMixture and MVTMixture representing stochastic mixtures over gaussian processes, student t processes and matrix variate t processes respectively.

Package dynaml.optimization

• Added abstract class MixtureMachine which takes as input a stochastic process model and returns a stochastic mixture model with weights computed using a grid search or coupled simulated annealing procedure.

• Added GPMixtureMachine an extension of MixtureMachine as a convenience class for creating gaussian process stochastic mixtures.

Package dynaml.kernels

• Added separable stationary kernel implementation in SeparableStationaryKernel
  as specified by Genton et. al.